{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json \n",
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>grad_norm</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>loss</th>\n",
       "      <th>step</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.008</td>\n",
       "      <td>2.230712</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>6.6702</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.016</td>\n",
       "      <td>1.510081</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>5.6233</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.024</td>\n",
       "      <td>1.472887</td>\n",
       "      <td>0.000395</td>\n",
       "      <td>5.2921</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.032</td>\n",
       "      <td>1.575634</td>\n",
       "      <td>0.000394</td>\n",
       "      <td>5.0924</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.040</td>\n",
       "      <td>1.467831</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>5.0049</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>1.968</td>\n",
       "      <td>0.822488</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>3.1379</td>\n",
       "      <td>2460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>1.976</td>\n",
       "      <td>0.801764</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>3.1833</td>\n",
       "      <td>2470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>1.984</td>\n",
       "      <td>0.812906</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>3.4549</td>\n",
       "      <td>2480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>1.992</td>\n",
       "      <td>0.847894</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>3.2866</td>\n",
       "      <td>2490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2.000</td>\n",
       "      <td>0.690124</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.6152</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     epoch  grad_norm  learning_rate    loss  step\n",
       "0    0.008   2.230712       0.000398  6.6702    10\n",
       "1    0.016   1.510081       0.000397  5.6233    20\n",
       "2    0.024   1.472887       0.000395  5.2921    30\n",
       "3    0.032   1.575634       0.000394  5.0924    40\n",
       "4    0.040   1.467831       0.000392  5.0049    50\n",
       "..     ...        ...            ...     ...   ...\n",
       "245  1.968   0.822488       0.000006  3.1379  2460\n",
       "246  1.976   0.801764       0.000005  3.1833  2470\n",
       "247  1.984   0.812906       0.000003  3.4549  2480\n",
       "248  1.992   0.847894       0.000002  3.2866  2490\n",
       "249  2.000   0.690124       0.000000  3.6152  2500\n",
       "\n",
       "[250 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_state_file = \"models/train_v1202/model_image-gpu2-large-zh-custom/trainer_state.json\"\n",
    "\n",
    "def load_loss_data(file):\n",
    "    with open(file, 'r') as fin:\n",
    "\n",
    "        alldata = \"\".join(fin.readlines())\n",
    "        data = json.loads(alldata)\n",
    "        data = data.get('log_history')\n",
    "\n",
    "    data = [i for i in data if i.get('loss') is not None]\n",
    "    return pd.DataFrame.from_dict(data)\n",
    "\n",
    "sample_data = load_loss_data(test_state_file)\n",
    "# len(sample_data), sample_data[:2]\n",
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_model_type = ['base', 'custom', 'gather', 'gather_nn']\n",
    "\n",
    "def load_loss_data_by_type(model_type):\n",
    "    file = f\"models/train_v1205/model_image-gpu2-large-zh-{model_type}/trainer_state.json\"\n",
    "    return load_loss_data(file)\n",
    "\n",
    "\n",
    "all_data = {model_type: load_loss_data_by_type(model_type) for model_type in train_model_type}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>epoch</th>\n",
       "      <th>grad_norm</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>loss</th>\n",
       "      <th>step</th>\n",
       "      <th>model_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000965</td>\n",
       "      <td>3.515733</td>\n",
       "      <td>3.998070e-04</td>\n",
       "      <td>5.0386</td>\n",
       "      <td>10</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001930</td>\n",
       "      <td>3.624786</td>\n",
       "      <td>3.996140e-04</td>\n",
       "      <td>3.3199</td>\n",
       "      <td>20</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002895</td>\n",
       "      <td>2.970568</td>\n",
       "      <td>3.994210e-04</td>\n",
       "      <td>2.5440</td>\n",
       "      <td>30</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003860</td>\n",
       "      <td>2.731416</td>\n",
       "      <td>3.992280e-04</td>\n",
       "      <td>2.2747</td>\n",
       "      <td>40</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004825</td>\n",
       "      <td>3.262762</td>\n",
       "      <td>3.990350e-04</td>\n",
       "      <td>2.1032</td>\n",
       "      <td>50</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067</th>\n",
       "      <td>1.995561</td>\n",
       "      <td>0.691053</td>\n",
       "      <td>8.877738e-07</td>\n",
       "      <td>0.1652</td>\n",
       "      <td>20680</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2068</th>\n",
       "      <td>1.996526</td>\n",
       "      <td>1.359571</td>\n",
       "      <td>6.947795e-07</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>20690</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2069</th>\n",
       "      <td>1.997491</td>\n",
       "      <td>1.769330</td>\n",
       "      <td>5.017852e-07</td>\n",
       "      <td>0.1537</td>\n",
       "      <td>20700</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>1.998456</td>\n",
       "      <td>1.964171</td>\n",
       "      <td>3.087909e-07</td>\n",
       "      <td>0.1559</td>\n",
       "      <td>20710</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>1.999421</td>\n",
       "      <td>1.297204</td>\n",
       "      <td>1.157966e-07</td>\n",
       "      <td>0.1110</td>\n",
       "      <td>20720</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8288 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         epoch  grad_norm  learning_rate    loss   step model_type\n",
       "0     0.000965   3.515733   3.998070e-04  5.0386     10       base\n",
       "1     0.001930   3.624786   3.996140e-04  3.3199     20       base\n",
       "2     0.002895   2.970568   3.994210e-04  2.5440     30       base\n",
       "3     0.003860   2.731416   3.992280e-04  2.2747     40       base\n",
       "4     0.004825   3.262762   3.990350e-04  2.1032     50       base\n",
       "...        ...        ...            ...     ...    ...        ...\n",
       "2067  1.995561   0.691053   8.877738e-07  0.1652  20680  gather_nn\n",
       "2068  1.996526   1.359571   6.947795e-07  0.1465  20690  gather_nn\n",
       "2069  1.997491   1.769330   5.017852e-07  0.1537  20700  gather_nn\n",
       "2070  1.998456   1.964171   3.087909e-07  0.1559  20710  gather_nn\n",
       "2071  1.999421   1.297204   1.157966e-07  0.1110  20720  gather_nn\n",
       "\n",
       "[8288 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_data_df = pd.concat([v.assign(**{\n",
    "    'model_type':k\n",
    "}) for k,v in all_data.items()])\n",
    "concat_data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>step</th>\n",
       "      <th>model_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0386</td>\n",
       "      <td>10</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.3199</td>\n",
       "      <td>20</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.5440</td>\n",
       "      <td>30</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.2747</td>\n",
       "      <td>40</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.1032</td>\n",
       "      <td>50</td>\n",
       "      <td>base</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2067</th>\n",
       "      <td>0.1652</td>\n",
       "      <td>20680</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2068</th>\n",
       "      <td>0.1465</td>\n",
       "      <td>20690</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2069</th>\n",
       "      <td>0.1537</td>\n",
       "      <td>20700</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2070</th>\n",
       "      <td>0.1559</td>\n",
       "      <td>20710</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2071</th>\n",
       "      <td>0.1110</td>\n",
       "      <td>20720</td>\n",
       "      <td>gather_nn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8288 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        loss   step model_type\n",
       "0     5.0386     10       base\n",
       "1     3.3199     20       base\n",
       "2     2.5440     30       base\n",
       "3     2.2747     40       base\n",
       "4     2.1032     50       base\n",
       "...      ...    ...        ...\n",
       "2067  0.1652  20680  gather_nn\n",
       "2068  0.1465  20690  gather_nn\n",
       "2069  0.1537  20700  gather_nn\n",
       "2070  0.1559  20710  gather_nn\n",
       "2071  0.1110  20720  gather_nn\n",
       "\n",
       "[8288 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concat_data_df[['loss', 'step', 'model_type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# 设置图形样式\n",
    "# plt.style.use('seaborn')\n",
    "# plt.figure(figsize=(10, 6))\n",
    "\n",
    "fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(10, 6))\n",
    "\n",
    "# 按model_type进行分组并绘制\n",
    "for name, group in concat_data_df.groupby('model_type'):\n",
    "    ax.plot(group['step'], group['loss'], label=name)\n",
    "\n",
    "ax.set_title('Loss vs Step by Model Type', fontsize=15)\n",
    "ax.set_xlabel('Step', fontsize=12)\n",
    "ax.set_ylabel('Loss', fontsize=12)\n",
    "ax.legend(title='Model Type')\n",
    "ax.grid(True, linestyle='--', alpha=0.7)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"loss_vs_step_by_model_type_big.png\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hz_torch_1031",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
